Markov Decision Processes

Examines several fundamentals concerning the manner in which Markov decision problems may be properly formulated and the determination of solutions or their properties. Coverage includes optimal equations, algorithms and their characteristics, probability distributions, modern development in the Markov decision process area, namely structural policy analysis, approximation modeling, multiple objectives and Markov games. Copiously illustrated with examples. A General Framework for Markov Decision Processes.

Algorithms.

Linear Programming Formulations for Markov DecisionProcesses.

Semi-Markov Decision Processes.

Partially Observable and Adaptive Markov Decision Processes.

Further Aspects of Markov Decision Processes.

Some Markov Decision Process Problems, Formulations and OptimalityEquations.

References.

Solutions to Exercises.

Index.

D. J. White is the author of Markov Decision Processes, published by Wiley.